code: def poisson(
      random_state,
      lam=1.0,
      size=None,
      chunk_size=None,
      gpu=None,
      dtype=None,
  ):
      r"""
      Draw samples from a Poisson distribution.
  
      The Poisson distribution is the limit of the binomial distribution
      for large N.
  
      Parameters
      ----------
      lam : float or array_like of floats
          Expectation of interval, should be >= 0. A sequence of expectation
          intervals must be broadcastable over the requested size.
      size : int or tuple of ints, optional
          Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
          ``m * n * k`` samples are drawn.  If size is ``None`` (default),
          a single value is returned if ``lam`` is a scalar. Otherwise,
          ``mt.array(lam).size`` samples are drawn.
      chunk_size : int or tuple of int or tuple of ints, optional
          Desired chunk size on each dimension
      gpu : bool, optional
          Allocate the tensor on GPU if True, False as default
      dtype : data-type, optional
        Data-type of the returned tensor.
  
      Returns
      -------
      out : Tensor or scalar
          Drawn samples from the parameterized Poisson distribution.
  
      Notes
      -----
      The Poisson distribution
  
      .. math:: f(k; \lambda)=\frac{\lambda^k e^{-\lambda}}{k!}
  
      For events with an expected separation :math:`\lambda` the Poisson
      distribution :math:`f(k; \lambda)` describes the probability of
      :math:`k` events occurring within the observed
      interval :math:`\lambda`.
  
      Because the output is limited to the range of the C long type, a
      ValueError is raised when `lam` is within 10 sigma of the maximum
      representable value.
  
      References
      ----------
      .. [1] Weisstein, Eric W. "Poisson Distribution."
             From MathWorld--A Wolfram Web Resource.
             http://mathworld.wolfram.com/PoissonDistribution.html
      .. [2] Wikipedia, "Poisson distribution",
             http://en.wikipedia.org/wiki/Poisson_distribution
  
      Examples
      --------
      Draw samples from the distribution:
  
      >>> import mars.tensor as mt
      >>> s = mt.random.poisson(5, 10000)
  
      Display histogram of the sample:
  
      >>> import matplotlib.pyplot as plt
      >>> count, bins, ignored = plt.hist(s.execute(), 14, normed=True)
      >>> plt.show()
  
      Draw each 100 values for lambda 100 and 500:
  
      >>> s = mt.random.poisson(lam=(100., 500.), size=(100, 2))
      """
      if dtype is None:
          dtype = (
              np.random.RandomState()
              .poisson(handle_array(lam), size=(0,))
              .dtype
          )
      size = random_state._handle_size(size)
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorPoisson(size=size, seed=seed, gpu=gpu, dtype=dtype)
      return op(lam, chunk_size=chunk_size)
  
file_path: mars/mars/tensor/random/poisson.py
imports: import numpy
  from ...None import opcodes
  from ...serialization.serializables import AnyField
  from ..utils import gen_random_seeds
  from .core import TensorRandomOperandMixin, handle_array, TensorDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: poisson(random_state, lam, size, chunk_size, gpu, dtype) and returns: op